CNNs are especially popular in computer vision, and they achieve high accuracies when detecting edges in images. The main idea behind their use for audio onset detection is that they can effectively detect edges in the magnitude spectrograms as well, which usually correspond to audio onsets. In this way, the usual input for the CNN is composed by three 80-band mel spectrograms with constant hop size but different window sizes so to have both high frequency and time resolution. The details of the original network's architecture can be found in [1]}. It was discovered in the same study that the CNN, like spectral flux based techniques, computes spectral differences over time and uses them to estimate the likelihood of an onset happening in a certain time region. The implementation of the CNN-based onset detection method, which this study follows, is contained in Madmom's CNNOnsetProcessor [2]}.
